#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
# @Author:      Bai Lingnan
# @Project:     Pytorch-Template
# @Filename:    util.py
# @Time:        2020/3/12 09:34
"""
import numpy as np
import torch


class EarlyStopping:
    """Early stops the training if validation loss doesn't improve after a given patience."""

    def __init__(self, opt, verbose=True, delta=0):
        """
        Args:
            patience (int): How long to wait after last time validation loss improved.
                            Default: 7
            verbose (bool): If True, prints a message for each validation loss improvement.
                            Default: False
            delta (float): Minimum change in the monitored quantity to qualify as an improvement.
                            Default: 0
        """
        self.patience = opt.patience
        self.verbose = verbose
        self.counter = 0
        self.best_score = None
        self.early_stop = False
        self.val_loss_min = np.Inf
        self.delta = delta

    def __call__(self, val_loss, model, opt):

        score = -val_loss

        if self.best_score is None:
            self.best_score = score
            self.save_checkpoint(val_loss, model, opt)
        elif score >= self.best_score + self.delta:
            self.counter += 1
            print(f"EarlyStopping counter: {self.counter} out of {self.patience}")
            if self.counter >= self.patience:
                self.early_stop = True
        else:
            self.best_score = score
            self.save_checkpoint(val_loss, model, opt)
            self.counter = 0

    def save_checkpoint(self, val_loss, model, opt):
        """Saves model when validation loss decrease."""
        if self.verbose:
            print(
                f"Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving model ..."
            )
        torch.save(
            model.state_dict(), opt.path_to_checkpoint + "lowest_val_loss_model.pt"
        )
        self.val_loss_min = val_loss
